As adults we know what an apple is because we understand it as a concept, the ideal "apple", and can manipulate the concept into areas way outside the original concept (say, the phrase "apple of my eye").
All they know is how to recognize a common pattern on a pixel grid, after seeing a large number of examples, and then draw a box around it.
The fact that a child has a body and can manipulate the world with all 5 senses working in concert should not be underestimated.
Very quickly (assuming said child doesn't eat something too bad), in the absence of an external oracle, the child learns a very productive mental model of what an apple is.
This type of feedback loop seems eminently translatable to machine learning, assuming we can encode the concept space in a way that allows the model to be encoded and trained in a reasonable set of constraints
The child develops concepts and is able to create and evaluate inferences, and thus able to understand metaphors etc.
The concept is what most AI approaches lack. Googles image search can identify apples, and cherries, and probably can categorize both as fruits, but it can't infer that this probably contains seeds, is a living being etc.
As I have an academic background in learning theory and developmental psychology, I'm pretty pessimistic about the current AI trend, autonomous driving etc. Most smart people in the field are chasing what are effectively more efficient regression functions for over 60 years now, and I almost never stumble upon approaches that have looked at what we know about actual human learning processes, development of the self etc.
Moravec's paradox[1] IMO should have been an inflection point for AI research. This is the level of problems AI research has to tackle if it ever wants to create AGI.